View source: R/globloc.rtests.R

global.rtest | R Documentation |

These two Monte Carlo tests are used to assess the existence of 'global' and
'local' spatial structures, corresponding respectively to positive and
negative Moran's I .

```
global.rtest(X, listw, k = 1, nperm = 499)
```

`X` |
a data matrix, with variables in columns |

`listw` |
a list of weights of class |

`k` |
integer: the number of highest |

`nperm` |
integer: the number of randomisations to be performed. |

They rely on the decomposition of a data matrix X into global and local
components using multiple regression on Moran's Eigenvector Maps (MEMs). They
require a data matrix (X) and a list of weights derived from a connection
network. X is regressed onto global MEMs (U+) in the global test and on local
ones (U-) in the local test. One mean `R^2`

is obtained for each
MEM, the k highest being summed to form the test statistic.

The reference distribution of these statistics are obtained by randomly permuting the rows of X.

These tests were originally part of the adegenet package for R.

An object of class `randtest`

.

Thibaut Jombart t.jombart@imperial.ac.uk

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. 2008.
Revealing cryptic spatial patterns in genetic variability by a new
multivariate method. *Heredity*, 101, 92–103. doi:
10.1038/hdy.2008.34.

```
# wait for a generic dataset
```

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